Discriminators make it possible to classify objects, that is to say to take a decision on the an object's membership in a class or in several predefined classes of objects. For example, the physical characteristics of the state of a patient are measured, such as his size, his weight, his age, his pulse and his body temperature. This patient is the query object or query patient. The measured values are thereafter provided to a discriminator, which compares these characteristics with those of other patients identified as suffering from such and such an illness. These other patients are the reference objects or reference patients. The illnesses are the classes. The discriminator assigns to the query patient the same illness as that of the closest reference patients in the sense of a proximity measurement based on the measured characteristics. Such applications are used as a decision aid, they can even make it possible to dispense with the opinion of an expert. But they can also provide the expert with a membership class which reinforces or otherwise his diagnosis of the patient and which may prompt him to a finer analysis in the case of disagreement. However, the intelligibility of the result of the discriminator is crucial in order that the expert can have confidence in the latter and that the aid is useful and effective.
In order to obtain the user's confidence, decision aid systems propose to associate a confidence indicator with the decision taken by the discriminator. The proposed decision is not explained, the system behaving as a black box, but it provides the user with an indicator presumed to reassure him regarding the quality of this decision. This indicator may be of probabilistic nature, or else obtained by relative comparison of the decisions of several different discriminators. In all cases, this indicator is obtained by a process that is relatively complex from the point of view of the non-specialist user of discrimination methods. In a certain manner, the discrimination system in the broad sense which provides both a decision and a confidence indicator for this decision is judge and jury, this not being apt to inspire the confidence of the user.
Other decision aid systems are based on explaining the decision taken by the discriminator in terms that are intelligible to the user. For example, fuzzy inference systems explain their decision as the result of a weighted sum of logical rules directly involving the original characteristics of the objects, these quantities and their combination being assumed to be easy for the user to interpret. However, to obtain a discrimination of good quality, the number of rules and parameters is often considerable, thereby greatly decreasing the intelligibility of the system.
Finally, other decision aid systems provide a probability of membership in various classes. In this case, the decision taken by the discriminator is not unique and the system does not decide on the assigning of a class in particular. The choice of a class for the query object is left to the astuteness of the user, who may be aided by these probabilities. However, the probabilities are assigned to the classes as a whole set, without explaining the process which makes it possible to pass from the reference objects to their membership classes. Thus, although the number of probabilities to be assessed is reduced, since it is equal to the number of classes, the process for calculating these probabilities remains unknown to the user. Therefore the information is overall very unintelligible to him.
In these three cases of discriminator according to the prior art, although the initial aim is to assist the work of the user, he is in reality asked to acquire an additional skill in the field of automatic discrimination. Indeed, the intelligibility of the system to the user depends on his understanding of the often sophisticated methods employed to generate the decision.
In addition to the use of discriminators to solve object classification problems, methods also exist for visualizing objects and classes so as to analyze the structure of classes or else to grade projections. These visualization methods may for example rely on methods for projecting the objects making it possible notably to represent the objects and the classes on a plane map. The position of the objects on this map is such that the objects which resemble one another according to a similarity measurement are rather close on the map. Reciprocally, objects that are not very similar are rather far apart on the map. A query object can then be positioned on this map via the same principle by taking account of its similarity to the already positioned reference objects. In addition to the technical problem of the positioning of this query object with respect to the others there arises the problem of interpreting this position in terms of membership class. Indeed, whether or not they take account of classes, projection methods induce a loss of information called false neighborhood or stitching, which artificially clusters objects which are in fact far apart according to the measurement of their similarity. These false neighborhoods exist either because it is technically impossible to adhere to the whole set of similarities during projection, or because although it is technically possible, the projection method did not know how to find this solution. Thus, the user may assign to a query object the majority class of the surrounding objects on the map, even if these objects are falsely close to the query object.
To attempt to surmount the problems related to false-neighborhoods, diagnostic methods making it possible to visualize these false-neighborhoods have been proposed, such as for example in the article “Visualizing distortions and recovering topology in continuous projection techniques” (Aupetit M., Neurocomputing, vol. 10, no. 7-9 pp. 1304-1330, 2007). Unfortunately, this method does not make it possible to evaluate the class of a query object with the help of reference objects belonging to known classes. Thus, when it is implemented to grade a projection, the method simply manipulates objects which are all “unlabeled”, whereas when it is implemented to analyze the structure of classes, the method simply manipulates objects which are all “labeled”.
Finally, it is also possible to visualize the reference objects close to the query object in the form of a list of reference objects ordered according to their decreasing proximities to the query object. Such is typically the case in search engines on the Internet network, where a query gives rise to the displaying of a list of reference Internet pages, ordered by proximity to the query. In this case, the list of reference objects that is presented to the user is dependent on the query used on the one hand, and exhibits a linear order on the other hand. In certain search engines on the Internet network, the query gives rise to the displaying of a set of ordered reference Internet pages in the forms of groups on a plane map which are emphasized graphically (color, size, etc.) so as to signify their proximity to the query. In this case again, the map presented to the user depends on the query. In all these cases, the list of reference objects presented, as well as their positions in the spatial (map) or linear (ordered list) representation, depend on the query. The user therefore cannot construct a stable mental representation of the universe of the reference objects, this universe never being presented to him in a manner which is complete or independent of the query. He cannot then by himself judge the quality of the information regarding proximity to the query, which is presented to him. Neither can he easily assess the resemblances or the differences between the query objects conveyed through their representation in terms of reference objects, since he has no fixed basis of comparison.